Didier Beloin-Saint-Pierre

The ESPA (Enhanced Structural Path Analysis) method: a solution to an implementation challenge for dynamic life cycle assessment studies

PurposeBy analyzing the latest developments in the dynamic life cycle assessment (DLCA) methodology, we identify an implementation challenge with the management of new temporal information to describe each system we might want to model. To address this problem, we propose a new method to differentiate elementary and process flows on a temporal level, and explain how it can generate temporally differentiated life cycle inventories (LCI), which are necessary inputs for dynamic impact assessment methods.MethodsFirst, an analysis of recent DLCA studies is used to identify the relevant temporal characteristics for an LCI. Then, we explain the implementation challenge of handling additional temporal information to describe processes in life cycle assessment (LCA) databases. Finally, a new format of temporal description is proposed to minimize the current implementation problem for DLCA studies.Results and discussionA new format of process-relative temporal distributions is proposed to obtain a temporal differentiation of LCA database information (elementary flows and product flows). A new LCI calculation method is also proposed since the new format for temporal description is not compatible with the traditional LCI calculation method. Description of the requirements and limits for this new method, named enhanced structural path analysis (ESPA), is also presented. To conclude the description of the ESPA method, we illustrate its use in a strategically chosen scenario. The use of the proposed ESPA method for this scenario reveals the need for the LCA community to reach an agreement on common temporal differentiation strategies for future DLCA studies.ConclusionsWe propose the ESPA method to obtain temporally differentiated LCIs, which should then require less implementation effort for the system-modeling step (LCA database definition), even if such concepts cannot be applied to every process.

Benefit of GEOSS Interoperability in Assessment of Environmental Impacts Illustrated by the Case of Photovoltaic Systems

Assessment of environmental impacts of a power system exploiting a renewable energy needs a large number of geographically-dependent data and of technological data. These data are located in various sources and available in various formats. To avoid the burden of data collection and reformatting, we exploit the interoperability capabilities set up in GEOSS and combine them with other GEOSS-compliant components proposed by projects funded by the European Commission. This is illustrated by the case of photovoltaic systems. A Web-based tool links the various sources of data and executes several models to offer various impacts factors in different areas: human health, climate change, primary energy, and ecosystems.

Implementing a Dynamic Life Cycle Assessment Methodology with a Case Study on Domestic Hot Water Production

This work contributes to the development of a dynamic life cycle assessment (DLCA) methodology by providing a methodological framework to link a dynamic system modeling method with a time‐dependent impact assessment method. This three‐step methodology starts by modeling systems where flows are described by temporal distributions. Then, a temporally differentiated life cycle inventory (TDLCI) is calculated to present the environmental exchanges through time. Finally, time‐dependent characterization factors are applied to the TDLCI to evaluate climate‐change impacts through time. The implementation of this new framework is illustrated by comparing systems producing domestic hot water (DHW) over an 80‐year period. Electricity is used to heat water in the first system, whereas the second system uses a combination of solar energy and gas to heat an equivalent amount of DHW at the same temperature. This comparison shows that using a different temporal precision (i.e., monthly vs. annual) to describe process flows can reverse conclusions regarding which case has the best environmental performance. Results also show that considering the timing of greenhouse gas (GHG) emissions reduces the absolute values of carbon footprint in the short‐term when compared with results from the static life cycle assessment. This pragmatic framework for the implementation of time in DLCA studies is proposed to help in the development of the methodology. It is not yet a fully operational scheme, and efforts are still required before DLCA can become state of practice.